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Individual Tree Crown Delineation for the Species Classification And drones Article Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images Anastasiia Safonova 1,2,*, Yousif Hamad 1,3, Egor Dmitriev 4,5, Georgi Georgiev 6, Vladislav Trenkin 7, Margarita Georgieva 6 , Stelian Dimitrov 8 and Martin Iliev 8 1 Deep Learning Laboratory, Siberian Federal University, 660074 Krasnoyarsk, Russia; [email protected] 2 Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain 3 College of Information Technology, Imam Ja’afar Al-Sadiq University, Kirkuk 661001, Iraq 4 Marchuk Institute of Numerical Mathematics Russian Academy of Sciences, 119333 Moscow, Russia; [email protected] 5 Institute for Scientific Research of Aerospace Monitoring “Aerocosmos”, State Scientific Institution, 105064 Moscow, Russia 6 Department of Forest Entomology, Phytopathology and Game Fauna, Forest Research Institute, Bulgarian Academy of Sciences, 1756 Sofia, Bulgaria; [email protected] (G.G.); [email protected] (M.G.) 7 Geografika Ltd., 1504 Sofia, Bulgaria; [email protected] 8 Department of Cartography and GIS, Faculty of Geology and Geography, Sofia University St. Kliment Ohridski, 1504 Sofia, Bulgaria; [email protected] (S.D.); [email protected] (M.I.) Citation: Safonova, A.; Hamad, Y.; * Correspondence: [email protected] Dmitriev, E.; Georgiev, G.; Trenkin, V.; Georgieva, M.; Dimitrov, S.; Iliev, M. Abstract: Monitoring the structure parameters and damage to trees plays an important role in forest Individual Tree Crown Delineation management. Remote-sensing data collected by an unmanned aerial vehicle (UAV) provides valuable for the Species Classification and resources to improve the efficiency of decision making. In this work, we propose an approach to Assessment of Vital Status of Forest enhance algorithms for species classification and assessment of the vital status of forest stands by Stands from UAV Images. Drones 2021, 5, 77. https://doi.org/ using automated individual tree crowns delineation (ITCD). The approach can be potentially used for 10.3390/drones5030077 inventory and identifying the health status of trees in regional-scale forest areas. The proposed ITCD algorithm goes through three stages: preprocessing (contrast enhancement), crown segmentation Academic Editors: Maggi Kelly and based on wavelet transformation and morphological operations, and boundaries detection. The per- Darren Turner formance of the ITCD algorithm was demonstrated for different test plots containing homogeneous and complex structured forest stands. For typical scenes, the crown contouring accuracy is about Received: 28 June 2021 95%. The pixel-by-pixel classification is based on the ensemble supervised classification method Accepted: 5 August 2021 error correcting output codes with the Gaussian kernel support vector machine chosen as a binary Published: 7 August 2021 learner. We demonstrated that pixel-by-pixel species classification of multi-spectral images can be performed with a total error of about 1%, which is significantly less than by processing RGB images. Publisher’s Note: MDPI stays neutral The advantage of the proposed approach lies in the combined processing of multispectral and RGB with regard to jurisdictional claims in photo images. published maps and institutional affil- iations. Keywords: remote sensing; pattern recognition; unmanned aerial vehicle; aerial photo and multi- spectral images; individual tree crowns delineation; species classification; vital status assessment Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. 1. Introduction This article is an open access article distributed under the terms and Forests are one of the most important components of the global ecosystem having a conditions of the Creative Commons significant impact on the Earth’s radiation balance, carbon cycle, and are of great economic Attribution (CC BY) license (https:// importance. The most accurate source of information on changes of the vital state and creativecommons.org/licenses/by/ inventory parameters of forest stands are ground-based surveys. An alternative to this ap- 4.0/). proach is the use of remote-sensing methods based on airborne and satellite measurements. Drones 2021, 5, 77. https://doi.org/10.3390/drones5030077 https://www.mdpi.com/journal/drones Drones 2021, 5, 77 2 of 18 With the advent of remote-sensing systems of very high spatial resolution, the problem of thematic mapping of forest areas at the level of individual trees arose. Prompt and accurate measurements of forest inventory parameters and vital status of individual trees are necessary for quantitative analysis of the forest structure, environmental modeling, and the assessment of biological productivity. The results obtained contribute to making more detailed thematic maps reflecting the local features of forest areas. Identifying trees from very high-resolution aerial images by looking at the form and structure of tree crowns is useful for the calculation of the forest density to control possible fire. In the past few years, unmanned imaging technologies have been widely developed for different remote-sensing tasks. In many ways, the development of unmanned aerial vehicle (UAV) imaging tools helps to complement satellite imagery for regional monitoring. In the last decade, we can find a number of works where methods of individual tree crown delineation (ITCD) based on data obtained from aerial photos and multi-spectral cameras as well as laser scanners (LiDARs) are successfully used for estimating structure parameters of forest stands (Jing et al., Ma et al. [1,2]). Such an analysis of individual tree crowns in digital images requires high spatial resolution. The results of ITCD obtained from high spatial resolution aerial images can be used in combination with high spectral resolution images for classifying tree species, growth rate, and regions of missing trees (Maschler et al. [3]). A similar approach can be employed to delineate tree growth areas to improve the results of thematic processing medium and high-resolution satellite images (Safonova et al., Dmitriev et al. [4,5]). Often, the satellite data do not determine the characteristics of the forest with the required accuracy. The improvement in the classification accuracy of dominant trees can be achieved by combining clearing mechanism data and horizontal raster clouds high- lighting sub-trees through the analysis of vertical features (Paris et al. [6]). The detection of individual trees on the aerial photography improves the management efficiency and production (Shahzad et al. [7]). ITCD methods based on the optical data (Wagner et al. [8]) or atmospheric laser (Ferraz et al. [9]) are being actively developed and used for tropical forests with high canopy density. Crown detection and segmentation have also been considered in northern and temperate forests (Morsdorf et al. [10]), and they perform better in coniferous forests than in mixed forests (Vega et al. [11]). Tree crowns are determined primarily by the image processing algorithms which are designed to detect edges and highlight some features (Torresan et al. [12]). Some of the forest parameters, such as crown area (Chemura et al. [13]), tree height, tree growth and crown cover are measured at the level of individual trees and require information on each tree. The accuracy of the separate tree crowns definition is related but not limited to the accuracy of subsequent species identification, gap analysis, and level-based assessment of characteristics such as above-ground biomass and forest carbon (Allouis et al. [14]). In Gupta et al. [15], the authors proposed to apply full waveforms to extract individual tree crowns using aggregated algorithms, but those did not show quantitative results due to the lack of local inventory data. Detection and segmentation of individual trees as a prerequisite for gathering accurate information about the structure of a forest (tree location, tree height) have received tremendous attention in the last few decades (Yang et al. [16]). A traditional method based on expert analysis of remote sensing data in combination with field measurements is still used to determine the forest parameters (Zhen et al. [17]). Due to increased levels of clogging with respect to land, tree crowns segmentation under a canopy in tall, dense forests remains an open problem. High-intensity UAV short- range drones can solve this problem to some extent. However, even if the segmentation of individual tree crowns is limited to subdominant trees, the crown size distribution provides new insights into tree architecture and improved carbon mapping capabilities that may be less dependent on local calibration than current region-based models (Coomes et al. [18]). The present paper aims to present an approach for the retrieval of forest parameters at the level of individual trees from very high spatial resolution UAV images. We propose Drones 2021, 5, x FOR PEER REVIEW 3 of 19 Drones 2021, 5, 77 The present paper aims to present an approach for the retrieval of forest parameters3 of 18 at the level of individual trees from very high spatial resolution UAV images. We propose an automated ITCD algorithm applicable to multispectral and RGB aerial images. The re- ansults automated obtained ITCDfrom the algorithm ITCD algorithm, applicable and to multispectralsupervised pixel and-by RGB-pixel aerial classification images. The are resultsused to obtained determine from individual
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